Let’s take the definitions from our last post for a test drive on tweeted percentiles for a hypothetical set of 100 papers, presented here in order of increasing readership with our assigned percentile ranges:

• 10 papers have 0 tweets (0-9th percentile)
• 40 papers have 1 tweet (10-49th)
• 10 papers have 2 tweets (50-59th)
• 20 papers have 5 tweets (60-79th)
• 1 paper has 9 tweets: (80th)
• 18 papers have 10 tweets (81-98th)
• 1 paper has 42 tweets (99th)

If someone came to us with a new paper that had 0 tweets, given the sample described above we would assign it to the 0-9th percentile (using a range rather than a single number because we roll like that).  A new paper with 1 tweet would be in the 10th-49th percentile.  A new paper with 9 tweets is easy: 80th percentile.

If we got a paper with 4 tweets we’d see it’s between the datapoints in our reference sample — the 59th and 60th percentiles — so we’d round down and report it as 59th percentile.  If someone arrives with a paper that has more tweets than anything in our collected reference sample we’d give it a 100th percentile.

Does this map to what you’d expect?  Our goal is to communicate accurate data as simply and intuitively as possible.  Let us know what you think!  @totalimpactorg on twitter, or team@total-impact.org.

(part 4 of a series on how total-impact plans to give context to the altmetrics it reports. see part 1, part 2, and part 3.)